218 citations · 220 across the 11 of their papers we have counts for
11 papers
SRPR-Net: Semantic and Relational Prompt Refinement for Automated SAM-based Instance Segmentation
Lufei Liu, Guojie Li, Suncheng Xiang +1
Instance segmentation is a fundamental computer vision task with diverse real-world applications. Recently, prompt-driven foundation models have shown promising generalization. How…
CoBa: Cost-Effective Test-Time Scaling via Compute-Balanced Routing
Yan Zhou, Yue Ouyang, Kaiyang Zheng +1
Test-time scaling is often implemented by spending more compute along one axis: sampling more solutions, extending a chain of thought, or applying a stronger evaluator. Under a fix…
MedCore: Boundary-Preserving Medical Core Pruning for MedSAM
Cenwei Zhang, Suncheng Xiang, Lei You
Medical segmentation foundation models such as SAM and MedSAM provide strong prompt-driven segmentation, but their image encoders are still too large for many clinical settings. Co…
BMDS-Net:Deployment-aware multi-modal brain tumor segmentation with adaptive fusion,decoder regularization,and Bayesian calibration
Yan Zhou, Suncheng Xiang, Zhen Huang +3
Multi-modal MRI enables detailed brain tumor sub-region segmentation, but clinical deployment remains affected by missing sequences,boundary errors, and overconfident predictions.…
GPF-Net: Gated Progressive Fusion Learning for Polyp Re-Identification
Suncheng Xiang, Xiaoyang Wang, Junjie Jiang +2
Colonoscopic Polyp Re-Identification (ReID) aims to match the same polyp across a large gallery of images captured from different viewpoints and with different cameras, playing a c…
MicroAUNet: Boundary-Enhanced Multi-scale Fusion with Knowledge Distillation for Colonoscopy Polyp Image Segmentation
Ziyi Wang, Yuanmei Zhang, Baoying Ye +3
Early and accurate segmentation of colorectal polyps is critical for reducing colorectal cancer mortality, which has been extensively explored by academia and industry. However, cu…
BALR-SAM: Boundary-Aware Low-Rank Adaptation of SAM for Resource-Efficient Medical Image Segmentation
Zelin Liu, Sicheng Dong, Bocheng Li +4
Vision foundation models like the Segment Anything Model (SAM), pretrained on large-scale natural image datasets, often struggle in medical image segmentation due to a lack of doma…
Leveraging MIMIC Datasets for Better Digital Health: A Review on Open Problems, Progress Highlights, and Future Promises
Afifa Khaled, Mohammed Sabir, Rizwan Qureshi +4
The Medical Information Mart for Intensive Care (MIMIC) datasets have become the Kernel of Digital Health Research by providing freely accessible, deidentified records from tens of…
Learning Collaborative Knowledge with Multimodal Representation for Polyp Re-Identification
Suncheng Xiang, Jiale Guan, Shilun Cai +2
Colonoscopic Polyp Re-Identification aims to match the same polyp from a large gallery with images from different views taken using different cameras, which plays an important role…
VM-UNet: Vision Mamba UNet for Medical Image Segmentation
Jiacheng Ruan, Jincheng Li, Suncheng Xiang
In the realm of medical image segmentation, both CNN-based and Transformer-based models have been extensively explored. However, CNNs exhibit limitations in long-range modeling cap…
Learning Discriminative Visual-Text Representation for Polyp Re-Identification
Suncheng Xiang, Cang Liu, Sijia Du +1
Colonoscopic Polyp Re-Identification aims to match a specific polyp in a large gallery with different cameras and views, which plays a key role for the prevention and treatment of…